Redcentric
Architect - AI Solutions
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Aim of the role:
The AI Solutions Architect will shape and enable practical AI adoption across Redcentric’s product and service portfolio, creating customer facing offerings and internal capabilities that improve service quality, automation, productivity and operational efficiency.
The role combines AI, data, automation, security, governance and modern delivery expertise with strong architectural leadership. It will translate AI opportunities into secure, governed, repeatable and commercially viable capabilities that can be delivered and operated at scale.
A key focus is embedding AI into existing products, services, operational capabilities and delivery practices, while also developing specific AI product offerings, internal tooling, process automation, knowledge enablement and engineering accelerators.
Key responsibilities:
AI Strategy, Architecture and Governance
- Shape Redcentric’s AI strategy, roadmap and capability development plan, identifying practical opportunities across customer services, internal operations, automation and knowledge management.
- Design secure, scalable and supportable AI solutions, translating business objectives into reusable architectures, delivery patterns and implementation guidance.
- Ensure AI adoption is governed, responsible and supportable, with appropriate controls for data protection, security, access, risk, auditability and operational ownership.
AI Product and Service Development
- Develop AI-enabled product and service offerings that enhance Redcentric’s existing portfolio and can be delivered repeatedly and commercially.
- Define offering scope, service components, delivery stages, support model, commercial assumptions, customer value proposition and route from pilot to production.
- Create reusable assets such as service definitions, reference architectures, implementation guides, risk controls and customer-facing technical content.
Internal Enablement and Delivery Excellence
- Identify and deliver opportunities for AI-enabled productivity, process automation, operational improvement and knowledge enablement.
- Support internal tooling, assistants, agents and workflows that reduce manual effort, improve quality and increase operational consistency.
- Promote repeatable, controlled and secure delivery practices across AI initiatives, including testing, validation, documentation and service transition.
Customer, Commercial and External Engagement
- Act as a trusted advisor to customers, supporting discovery, use-case prioritisation, solution design and controlled adoption of AI capabilities.
- Provide technical leadership for AI-related bids, proposals, workshops, commercial opportunities and customer engagements.
- Maintain awareness of emerging AI capabilities, operating models, governance approaches and market trends to inform Redcentric’s propositions and delivery approach.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
This list of responsibilities is not exhaustive, and the role holder is expected to reasonably take on any other responsibilities required to support business activities within the Redcentric Group.
Success Measures
A successful AI Solutions Architect will:
- Deliver repeatable AI architectures, delivery patterns and implementation frameworks.
- Define and mature specific AI product offerings with clear service, commercial and operational models.
- Embed AI into existing Redcentric services and internal operating capabilities.
- Improve automation, documentation, knowledge management, engineering productivity and operational efficiency.
- Support secure, governed and supportable AI adoption through modern delivery and assurance practices.
- Contribute to pipeline growth, customer satisfaction, service innovation and technical differentiation.
Person specification
The ideal candidate will be able to demonstrate the following skills and experience:
Professional Attributes
A successful AI Solutions Architect will:
- Turn emerging AI concepts into practical, supportable and repeatable capabilities.
- Take a portfolio-minded approach to enhancing existing services rather than treating AI as a standalone technology.
- Collaborate effectively across Product, Architecture, Engineering, Security, Assurance, Service Delivery, Operations and Commercial teams.
- Communicate complex AI concepts clearly to technical and non-technical audiences.
- Balance innovation with governance, risk, customer trust, commercial viability and operational supportability.
- Show curiosity, pragmatism and a strong bias toward measurable outcomes, reuse and continuous improvement.
Technical Expertise
The successful candidate should demonstrate strong knowledge across the following broad areas:
Artificial Intelligence & Automation
- Generative AI, foundation models, large language models, AI assistants, agents and conversational interfaces.
- Retrieval Augmented Generation, enterprise search, knowledge assistants, prompt lifecycle, orchestration and workflow automation.
- AI-enabled service management, documentation, knowledge management, use-case prioritisation, responsible AI and governance.
Platform and Service Architecture
- Platform, service and solution architecture principles, including connectivity, integration, dependency mapping and interoperability.
- Identity, access, policy, governance, compliance, secure configuration and sensitive information handling.
- Scalable deployment patterns, monitoring, observability, cost, performance, resilience and service management considerations.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Workplace, Collaboration and Knowledge Systems
- Workplace, collaboration, document management and knowledge sharing patterns.
- Enterprise information models, integration concepts, information governance, compliance and records management.
- Knowledge discovery, productivity enablement, information protection, classification, identity and user access considerations.
Data & Integration
- Data architecture, governance, quality, access, lifecycle and classification considerations.
- Structured and unstructured data, document processing, knowledge retrieval, indexing and search patterns.
- Integration, API, enterprise application and interoperability concepts.
Modern Engineering
- Modern delivery, engineering, automation, orchestration and operational practices.
- Repeatable environment management, controlled release, change, deployment and lifecycle practices.
- Secure delivery, testing, validation, assurance, observability, reliability, service transition and operational readiness.
Security & Governance
- Security architecture, identity security, data protection, privacy, regulatory compliance and secure-by-design principles.
- AI risk management, governance, auditability, logging, monitoring and assurance controls.
- Supplier, third-party, customer data isolation and boundary considerations.
Experience
Desirable experience:
- Designing and delivering enterprise-scale technology, automation, data, AI-enabled or software-integrated solutions.
- Working in customer-facing architecture, consultancy or pre-sales roles.
- Translating business requirements into solution designs, roadmaps, operating models and delivery plans.
- Creating reference architectures, implementation patterns, proposal content and reusable delivery frameworks.
- Applying modern delivery, secure engineering, automation, release management or platform operating practices.
- Working with security, governance, compliance, service design and operational transition teams.
- Shaping commercial opportunities, bids, statements of work, managed service propositions or customer workshops.
- Defining, packaging or launching repeatable technology product offerings, accelerators or customer-facing propositions.
Qualifications & Certifications
Desirable certifications include:
- Architecture, service design or enterprise architecture certifications
- AI, machine learning or data-related certifications
- Security, privacy, risk or governance certifications
- Modern delivery, service management or operational excellence certifications
- Responsible AI, AI governance or data protection training
- Relevant industry or professional certifications aligned to the role scope
Equivalent experience may be considered in lieu of certifications.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location